code: def test_what(tmp_path, verbose_debug):
      """Test mne.what."""
      pytest.importorskip("sklearn")
      # ICA
      ica = ICA(max_iter=1)
      raw = RawArray(
          np.random.RandomState(0).randn(3, 10),
          create_info(3, 1000.0, "eeg"),
      )
      with _record_warnings():  # convergence sometimes
          ica.fit(raw)
      fname = tmp_path / "x-ica.fif"
      ica.save(fname)
      assert what(fname) == "ica"
      # test files
      fnames = glob.glob(str(data_path / "MEG" / "sample" / "*.fif"))
      fnames += glob.glob(
          str(data_path / "subjects" / "sample" / "bem" / "*.fif")
      )
      fnames = sorted(fnames)
      want_dict = dict(
          eve="events",
          ave="evoked",
          cov="cov",
          inv="inverse",
          fwd="forward",
          trans="transform",
          proj="proj",
          raw="raw",
          meg="raw",
          sol="bem solution",
          bem="bem surfaces",
          src="src",
          dense="bem surfaces",
          sparse="bem surfaces",
          head="bem surfaces",
          fiducials="fiducials",
      )
      for fname in fnames:
          kind = Path(fname).stem.split("-")[-1]
          if len(kind) > 5:
              kind = kind.split("_")[-1]
          this = what(fname)
          assert this == want_dict[kind]
      fname = (
          data_path / "MEG" / "sample" / "sample_audvis-ave_xfit.dip"
      )
      assert what(fname) == "unknown"
  
file_path: mne-python/mne/_fiff/tests/test_what.py
imports: import glob
  from pathlib import Path
  import numpy
  import pytest
  from mne import create_info, what
  from mne.datasets import testing
  from mne.io import RawArray
  from mne.preprocessing import ICA
  from mne.utils import _record_warnings
label_desc: Use image node_id 1 for calling a global function with example usage: test_what(tmp_path, verbose_debug) without return types